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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/100832

    Title: A Two-Stage Cardholder Behavioral scoring Model Using Artificial Neural Networks and data Envelopment Analysis
    Authors: I-Fei Chen
    Contributors: 管理科學學系暨研究所
    Keywords: Chi-square Automatic Interaction Detector (CHAID);Artificial Neural Networks (ANNs);Data Envelopment Analysis (DEA);Behavioural Scoring;Data Mining
    Date: 2011-03
    Issue Date: 2015-03-16 10:09:20 (UTC+8)
    Abstract: Since the databases that banks use for analysis of cardholders’ repayment behaviours are usually
    large and complicated, and the extant classification techniques hardly offer 100% correct
    classification accuracy so as to possibly incur a considerable loss associated with type II errors, the
    prediction of cardholders’ future payment behaviours has been still referred to as a difficult task in the
    credit industry.
    This paper proposes a two-stage cardholder behavioural scoring model, with merits of artificial
    neural networks (ANNSs) and data envelopment analysis (DEA), which not only enables banks to
    verify the ANNSs predicted results of each cardholder’s future repayment behaviour as well as to
    identify creditworthy cardholders who is profitable with low risks, but also provides guidelines to
    improve contributions of each inefficient cardholder for card issuer profitability.
    Relation: International Journal of Advancements in Computing Technology 3(2)
    DOI: 10.4156/ijact.vol3.issue2.11
    Appears in Collections:[管理科學學系暨研究所] 期刊論文

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